Ras Bodik

To explain causality in biological systems, we want our models to be mechanistic, i.e., reproducing the behavior by simulating the underlying signaling mechanisms.Because most mechanistic models are executable programs, we can in principle infer them from experimental data using program synthesis.Unfortunately, the data is typically too under-constrained to induce a model on its own.I will show how to phrase synthesis so that experimental data can be complemented with diverse prior information, including expert’s assumptions; and how to compute experiments that would rule out some plausible models.I will draw lessons from two case studies: cell fate determination in c. elegans VPC cells and EGF signaling.I will conclude by outlining some open problems for the future of modeling where inference will be made from a union of qualitatively different experiments.